Pub Date : 2019-10-01DOI: 10.1109/SmartGridComm.2019.8909780
R. R. Kumar, D. Kundur, B. Sikdar
Centralized controllers are popularly used in Microgrid as it ensures its economic and stable operation. The measurements taken for such a controller are prone to false data injection (FDI) attacks which may result in destabilizing the microgrid. This paper presents a technique that uses transient information for detecting the FDI attacks in a microgrid. The detection technique works on the principle that any legitimate change in the system will be accompanied by a transient that can be observed by the measurement system. The transient solution is obtained using a backward forward sweep technique which is developed in this paper. This technique is much efficient than the Electromagnetic Transient Program (EMTP) as it solves the dynamic equations by exploiting the radial feature of the microgrid network. The solution is compared against the measured values such that in the event of an FDI attack, transients may not be present and hence it will have high deviations. The proposed technique is evaluated on a microgrid under the FDI attack and the results are presented.
{"title":"Transient Model-Based Detection Scheme for False Data Injection Attacks in Microgrids","authors":"R. R. Kumar, D. Kundur, B. Sikdar","doi":"10.1109/SmartGridComm.2019.8909780","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2019.8909780","url":null,"abstract":"Centralized controllers are popularly used in Microgrid as it ensures its economic and stable operation. The measurements taken for such a controller are prone to false data injection (FDI) attacks which may result in destabilizing the microgrid. This paper presents a technique that uses transient information for detecting the FDI attacks in a microgrid. The detection technique works on the principle that any legitimate change in the system will be accompanied by a transient that can be observed by the measurement system. The transient solution is obtained using a backward forward sweep technique which is developed in this paper. This technique is much efficient than the Electromagnetic Transient Program (EMTP) as it solves the dynamic equations by exploiting the radial feature of the microgrid network. The solution is compared against the measured values such that in the event of an FDI attack, transients may not be present and hence it will have high deviations. The proposed technique is evaluated on a microgrid under the FDI attack and the results are presented.","PeriodicalId":377150,"journal":{"name":"2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123763169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-01DOI: 10.1109/SmartGridComm.2019.8909756
Lingling Tang, Yulin Yi, Yuexing Peng
Electrical load forecasting is an important part of power system planning and operation, which can guide the power enterprises to arrange generation plan reasonably, reduce the cost of power generation, and provide a reference for power grid reconstruction and optimization. However, due to the complicated inner non-linear property and seasonality pattern of electrical load, accurate short-term load forecasting (STLF) is of big challenge. In this paper, we firstly study the large time-span quasi-periodicity of load sequences, including the inner correlation of a short load segment and the quasi-periodicity among the load segments spanning different time duration from a week to a month. Then, an ensemble method is proposed, which combines Auto-regressive Integrated Moving Average (ARIMA) and Long Short Term Memory (LSTM) in order to fully exploit the large time-span quasi-periodicity of the loads. Here, ARIMA model captures the stationary pattern of the load segments, while LSTM extracts the complicated non-linear relations of load segments. The proposed method is evaluated on a data set of load consumption in Toronto, and the results show the proposed method outperforms the existing popular STLF models with a small payload of computational complexity.
{"title":"An ensemble deep learning model for short-term load forecasting based on ARIMA and LSTM","authors":"Lingling Tang, Yulin Yi, Yuexing Peng","doi":"10.1109/SmartGridComm.2019.8909756","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2019.8909756","url":null,"abstract":"Electrical load forecasting is an important part of power system planning and operation, which can guide the power enterprises to arrange generation plan reasonably, reduce the cost of power generation, and provide a reference for power grid reconstruction and optimization. However, due to the complicated inner non-linear property and seasonality pattern of electrical load, accurate short-term load forecasting (STLF) is of big challenge. In this paper, we firstly study the large time-span quasi-periodicity of load sequences, including the inner correlation of a short load segment and the quasi-periodicity among the load segments spanning different time duration from a week to a month. Then, an ensemble method is proposed, which combines Auto-regressive Integrated Moving Average (ARIMA) and Long Short Term Memory (LSTM) in order to fully exploit the large time-span quasi-periodicity of the loads. Here, ARIMA model captures the stationary pattern of the load segments, while LSTM extracts the complicated non-linear relations of load segments. The proposed method is evaluated on a data set of load consumption in Toronto, and the results show the proposed method outperforms the existing popular STLF models with a small payload of computational complexity.","PeriodicalId":377150,"journal":{"name":"2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134532263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-01DOI: 10.1109/SmartGridComm.2019.8909783
P. Biswas, Heng Chuan Tan, Qingbo Zhu, Yuan Li, D. Mashima, Binbin Chen
Cyber attacks pose a major threat to smart grid infrastructures where communication links bind physical devices to provide critical measurement, protection, and control functionalities. Substation is an integral part of a power system. Modern substations with intelligent electronic devices and remote access interface are more prone to cyber attacks. Hence, there is an urgent need to consider cybersecurity at the electrical substation level. This paper makes a systematic effort to develop a synthesized dataset focusing on IEC 61850 GOOSE communication that is essential for automation and protection in smart grid. The dataset is intended to facilitate the research community to study the cybersecurity of substations. We present the physical system of a typical distribution level substation and several of its critical electrical protection operation scenarios under different disturbances, followed by several cyber-attack scenarios. We have generated a dataset with multiple traces that correspond to these scenarios and demonstrated how the dataset can be used to support substation cybersecurity research.
{"title":"A Synthesized Dataset for Cybersecurity Study of IEC 61850 based Substation","authors":"P. Biswas, Heng Chuan Tan, Qingbo Zhu, Yuan Li, D. Mashima, Binbin Chen","doi":"10.1109/SmartGridComm.2019.8909783","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2019.8909783","url":null,"abstract":"Cyber attacks pose a major threat to smart grid infrastructures where communication links bind physical devices to provide critical measurement, protection, and control functionalities. Substation is an integral part of a power system. Modern substations with intelligent electronic devices and remote access interface are more prone to cyber attacks. Hence, there is an urgent need to consider cybersecurity at the electrical substation level. This paper makes a systematic effort to develop a synthesized dataset focusing on IEC 61850 GOOSE communication that is essential for automation and protection in smart grid. The dataset is intended to facilitate the research community to study the cybersecurity of substations. We present the physical system of a typical distribution level substation and several of its critical electrical protection operation scenarios under different disturbances, followed by several cyber-attack scenarios. We have generated a dataset with multiple traces that correspond to these scenarios and demonstrated how the dataset can be used to support substation cybersecurity research.","PeriodicalId":377150,"journal":{"name":"2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133036945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-01DOI: 10.1109/SmartGridComm.2019.8909725
S. Barker, Kyle Morrison, Tucker Williams
The recent explosion of interest in smart building energy-efficiency has led to a proliferation of public energy datasets. Most of these datasets focus on depth (i.e., many devices in a few buildings) as opposed to breadth (e.g., a few devices in many buildings), and thus most smart building algorithms are evaluated on depth-oriented datasets. We argue that increasing data breadth conveys important benefits that are not easily achieved by even a large quantity of deep data. As an illustrative case study, we consider the problem of classifying previously unseen appliances using an off-the-shelf classifier trained on known instances of other devices. Our experiments on multiple real-world datasets (both depth- and breadth-oriented) demonstrate significant and sustained benefits from increased data breadth, and point to the importance of incorporating greater breadth into similar techniques that rely on generalized electrical load models.
{"title":"Exploiting Breadth in Energy Datasets for Automated Device Identification","authors":"S. Barker, Kyle Morrison, Tucker Williams","doi":"10.1109/SmartGridComm.2019.8909725","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2019.8909725","url":null,"abstract":"The recent explosion of interest in smart building energy-efficiency has led to a proliferation of public energy datasets. Most of these datasets focus on depth (i.e., many devices in a few buildings) as opposed to breadth (e.g., a few devices in many buildings), and thus most smart building algorithms are evaluated on depth-oriented datasets. We argue that increasing data breadth conveys important benefits that are not easily achieved by even a large quantity of deep data. As an illustrative case study, we consider the problem of classifying previously unseen appliances using an off-the-shelf classifier trained on known instances of other devices. Our experiments on multiple real-world datasets (both depth- and breadth-oriented) demonstrate significant and sustained benefits from increased data breadth, and point to the importance of incorporating greater breadth into similar techniques that rely on generalized electrical load models.","PeriodicalId":377150,"journal":{"name":"2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126557399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-01DOI: 10.1109/SmartGridComm.2019.8909720
Hailing Zhu, K. Ouahada
In this paper, energy storage sharing among a group of households with integrated renewable generations in a grid-connected microgrid is studied. In such a microgrid, a shared energy storage management (SESM) system is operated by an aggregator aiming to minimize the long term time-averaged costs of all households, by jointly taking into account the operational constraints of the shared energy storage, the stochastic solar power generations and the time-varying load requests from all households, as well as the fluctuating electricity prices. We formulate this energy management problem, which comprises storage management and load control, as a constrained stochastic programming problem. A centralized real-time sharing control algorithm is designed based on the Lyapunov optimization theory. The performance of the proposed real-time sharing control algorithm is evaluated. By comparing with a greedy sharing algorithm, it is shown that the proposed sharing algorithm outperforms in terms of both cost saving and renewable energy generation utilization.
{"title":"Cost Minimization Energy Storage Sharing Management","authors":"Hailing Zhu, K. Ouahada","doi":"10.1109/SmartGridComm.2019.8909720","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2019.8909720","url":null,"abstract":"In this paper, energy storage sharing among a group of households with integrated renewable generations in a grid-connected microgrid is studied. In such a microgrid, a shared energy storage management (SESM) system is operated by an aggregator aiming to minimize the long term time-averaged costs of all households, by jointly taking into account the operational constraints of the shared energy storage, the stochastic solar power generations and the time-varying load requests from all households, as well as the fluctuating electricity prices. We formulate this energy management problem, which comprises storage management and load control, as a constrained stochastic programming problem. A centralized real-time sharing control algorithm is designed based on the Lyapunov optimization theory. The performance of the proposed real-time sharing control algorithm is evaluated. By comparing with a greedy sharing algorithm, it is shown that the proposed sharing algorithm outperforms in terms of both cost saving and renewable energy generation utilization.","PeriodicalId":377150,"journal":{"name":"2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126589760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-01DOI: 10.1109/smartgridcomm.2019.8909738
{"title":"SmartGridComm 2019 Committees","authors":"","doi":"10.1109/smartgridcomm.2019.8909738","DOIUrl":"https://doi.org/10.1109/smartgridcomm.2019.8909738","url":null,"abstract":"","PeriodicalId":377150,"journal":{"name":"2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"220 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129876681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-01DOI: 10.1109/SmartGridComm.2019.8909771
A. Sebastian, S. Islam, Md. Apel Mahmud, A. Oo
In this paper, the energy sell/purchase for local energy trading is optimized to minimize the energy mismatch in a microgrid. In this microgrid, three groups of houses have been considered, those who have no solar panel or storage, those who have solar panels but no storage and those who have both the solar panel and storage. The houses are also equipped with load shifting capacity. For each of the houses, the difference in energy generation and consumption is forwarded to the control centre and the control centre optimizes the energy trading after which the outcome of the optimization is transmitted to individual houses. Different priorities are assigned to houses according to their consumption pattern and the corresponding optimization problems are solved in multiple stages accordingly. To evaluate the effectiveness of the proposed approach through numerical simulation, four different scenarios were considered with different energy consumption patterns and load shifting at different houses. The simulation results demonstrate that the proposed approach can achieve a reduction in energy mismatch up to 1000W during 09.00 a.m. to 12.00 p.m. for a microgrid with six houses and is more effective compared to the schemes where priorities are not assigned to participants. Moreover, the energy mismatch is further reduced when energy trading is jointly implemented with load shifting.
{"title":"Optimum Local Energy Trading considering Priorities in a Microgrid","authors":"A. Sebastian, S. Islam, Md. Apel Mahmud, A. Oo","doi":"10.1109/SmartGridComm.2019.8909771","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2019.8909771","url":null,"abstract":"In this paper, the energy sell/purchase for local energy trading is optimized to minimize the energy mismatch in a microgrid. In this microgrid, three groups of houses have been considered, those who have no solar panel or storage, those who have solar panels but no storage and those who have both the solar panel and storage. The houses are also equipped with load shifting capacity. For each of the houses, the difference in energy generation and consumption is forwarded to the control centre and the control centre optimizes the energy trading after which the outcome of the optimization is transmitted to individual houses. Different priorities are assigned to houses according to their consumption pattern and the corresponding optimization problems are solved in multiple stages accordingly. To evaluate the effectiveness of the proposed approach through numerical simulation, four different scenarios were considered with different energy consumption patterns and load shifting at different houses. The simulation results demonstrate that the proposed approach can achieve a reduction in energy mismatch up to 1000W during 09.00 a.m. to 12.00 p.m. for a microgrid with six houses and is more effective compared to the schemes where priorities are not assigned to participants. Moreover, the energy mismatch is further reduced when energy trading is jointly implemented with load shifting.","PeriodicalId":377150,"journal":{"name":"2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128337052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Vulnerability of various machine learning methods to adversarial examples has been recently explored in the literature. Power systems which use these vulnerable methods face a huge threat against adversarial examples. To this end, we first propose a more accurate and computationally efficient method called Adaptive Normalized Attack (ANA) to attack power systems using generate adversarial examples. We then adopt adversarial training to defend against attacks of adversarial examples. Experimental analyses demonstrate that our attack method provides less perturbation compared to the state-of-the-art FGSM (Fast Gradient Sign Method) and DeepFool, while our proposed method increases misclassification rate of learning methods for attacking power systems. In addition, the results show that the proposed adversarial training improves robustness of power systems to adversarial examples compared to using state-of-the-art methods.
{"title":"Adaptive Normalized Attacks for Learning Adversarial Attacks and Defenses in Power Systems","authors":"Jiwei Tian, Tengyao Li, Fute Shang, Kunrui Cao, Jing Li, M. Ozay","doi":"10.1109/SmartGridComm.2019.8909713","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2019.8909713","url":null,"abstract":"Vulnerability of various machine learning methods to adversarial examples has been recently explored in the literature. Power systems which use these vulnerable methods face a huge threat against adversarial examples. To this end, we first propose a more accurate and computationally efficient method called Adaptive Normalized Attack (ANA) to attack power systems using generate adversarial examples. We then adopt adversarial training to defend against attacks of adversarial examples. Experimental analyses demonstrate that our attack method provides less perturbation compared to the state-of-the-art FGSM (Fast Gradient Sign Method) and DeepFool, while our proposed method increases misclassification rate of learning methods for attacking power systems. In addition, the results show that the proposed adversarial training improves robustness of power systems to adversarial examples compared to using state-of-the-art methods.","PeriodicalId":377150,"journal":{"name":"2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132174033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-01DOI: 10.1109/SmartGridComm.2019.8909816
Yunxiang Wu, Qinghua Xu, S. Shao, Hongpin Zhao, G. Chang, Yang Liu
This paper presents a common DC system employed in a research vessel, showing the advantages of smaller size, less fuel consumption and better control performance. Detailed design considerations are discussed, followed by experimental verification and field test results. The proposed system can achieve stable and flexible operation.
{"title":"Design and Practical Verification of a Common DC Bus Power System in a Research Vessel","authors":"Yunxiang Wu, Qinghua Xu, S. Shao, Hongpin Zhao, G. Chang, Yang Liu","doi":"10.1109/SmartGridComm.2019.8909816","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2019.8909816","url":null,"abstract":"This paper presents a common DC system employed in a research vessel, showing the advantages of smaller size, less fuel consumption and better control performance. Detailed design considerations are discussed, followed by experimental verification and field test results. The proposed system can achieve stable and flexible operation.","PeriodicalId":377150,"journal":{"name":"2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"171 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133640256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-01DOI: 10.1109/SmartGridComm.2019.8909708
Shiyao Zhang, Ka-Cheong Leung
Electric Bus (EB), as a special type of electric vehicle (EV), can be regarded as a large mobile storage in the city. With large batteries installed, EBs are capable of providing frequency regulation service to the local power grid of the city. In this paper, a novel framework for joint optimal allocation and scheduling for EBs with V2G regulation service is studied. First, the optimal allocation and scheduling problem is proposed as a mixed-integer quadratic programming (MIQP) problem. Second, by using the Lagrangian dual decomposition method, a distributed algorithm is devised to solve this problem. The simulation results show that both the optimal allocation to balance EB demand in the city and scheduling for V2G regulation service can be achieved in an effective manner. In addition, with more EBs participated in the system, the power fluctuations of local city power grid can be further flattened.
{"title":"Joint Optimal Allocation and Scheduling for Electric Buses with Vehicle-to-Grid Regulation Service","authors":"Shiyao Zhang, Ka-Cheong Leung","doi":"10.1109/SmartGridComm.2019.8909708","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2019.8909708","url":null,"abstract":"Electric Bus (EB), as a special type of electric vehicle (EV), can be regarded as a large mobile storage in the city. With large batteries installed, EBs are capable of providing frequency regulation service to the local power grid of the city. In this paper, a novel framework for joint optimal allocation and scheduling for EBs with V2G regulation service is studied. First, the optimal allocation and scheduling problem is proposed as a mixed-integer quadratic programming (MIQP) problem. Second, by using the Lagrangian dual decomposition method, a distributed algorithm is devised to solve this problem. The simulation results show that both the optimal allocation to balance EB demand in the city and scheduling for V2G regulation service can be achieved in an effective manner. In addition, with more EBs participated in the system, the power fluctuations of local city power grid can be further flattened.","PeriodicalId":377150,"journal":{"name":"2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116315232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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